System and method for integrating retail price optimization for revenue and profit with business rules

ABSTRACT

The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/795,074, entitled “SYSTEM AND METHOD FOR INTEGRATINGRETAIL PRICE OPTIMIZATION FOR REVENUE AND PROFIT WITH BUSINESS RULES”,filed on Oct. 10, 2012, and which is hereby expressly incorporatedherein by reference in its entirety.

FIELD OF INVENTION

The present disclosure relates to generating recommended prices, andmore specifically pertains to recommended prices that effectively tradeoff revenue-profit values and retail-price business rules.

BACKGROUND

There has been a steady evolution in retail science towards solutionswith more sensitivity to business reality. Early mathematical solutionsin price optimization made retailers more revenue and profit but oftengenerated solutions that were in conflict with business rules. Forexample, in most retail settings the double-size box should be more thanthe single-size box, but not quite twice as much. Newer priceoptimization techniques involve performing business-rule compliance as apost-process procedure, treating business rules as absolute constraintsand resolving unavoidable conflict with rule prioritization, or leavingbusiness-rule compliance as a user task. These techniques can producecalculations that are difficult and often not followed and can lead tosub-optimal recommended prices.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readablestorage media for a price optimization technique that can be used ingenerating recommended prices for retail products. Using a demand model,prices can be optimized within product networks based on a tradeoff ofrevenue, profit, and one or more business constraints. The result is aset of recommended prices in which one or more products can be assigneda price different than an original price of the product. In theory, suchan approach produces an ideal set of prices that maximize revenue and/orprofit. However, the set of recommended prices may be unworkable in arealistic business setting.

One problem that storeowners can face is that the generated recommendedprices can violate one or more business rules. The business rules cangovern a single price, such as minimum or maximum price change, margin,locked prices, competitive prices, ending numbers, and multiples. Thebusiness rules can also define relationships between different productsthus involving two decision prices, such as size, brand, product line,and promotion. Determining recommended prices becomes more difficultwith each business rule that needs to be followed. For example, pricerelationships between different sizes of the same product and different,interchangeable brands can make it difficult to optimize revenue andprofit. The presently disclosed price optimization technique addressesthis limitation by assigning a reasonable monetary value to each ruleand resolving the combination of revenue, profit, and rule compliance.That is, the price optimization technique can generate recommended pricesets that optimize revenue and profit while complying with a set ofbusiness rules by assigning a monetary value to each business rule.

The disclosed price optimization technique can be applied to networks ofrule-related product prices. In general, the present price optimizationtechnique generates a set of recommended prices for a collection ofproduct networks by first obtaining constraint determined networks, orproduct networks. The technique then generates a collection ofprice-optimized solution sets for a product network by optimizing adecision price set for the product network based on one or morerevenue-profit weights. The technique optimizes the decision price setby repeatedly selecting a revenue-profit weight. For each selectedrevenue-profit weight, the technique generates a constrained bestsolution set and an unconstrained best solution set. The techniquegenerates the constrained best solution set based on a first set ofbusiness rules that include at least one flexible or optional businessrules, e.g., maximum price change, margin, competitive pricing, size,brand, product line, and promotion; and the unconstrained best solutionset based on a second set of business rules. Both the first and secondset of business rules can include non-negotiable or hard business rules,e.g., minimum price change, locked process, ending numbers, andmultiples. The constrained best solution is the recommend price set fora selected weight and the unconstrained best solution is the optimalprice set for the selected weight.

The technique generates a best solution set, either constrained orunconstrained, by calculating a current monetary value based on anobtained current price set. For example, for a revenue-profit weight,monetary value can be defined as Value=((1−W)×Revenue)+(W×Profit)). Thetechnique also obtains initially optimized prices for the selectedrevenue-profit weight and calculates an initially optimized monetaryvalue based on the initially optimized prices. The technique obtainsboth a current monetary value and an initially optimized monetary valueto identify which is better. Once the technique has both the currentmonetary value and the initially optimized monetary value, the techniquechecks if the initially optimized monetary value is less than thecurrent monetary value. If so, the technique sets the current prices asthe initial decision prices for the product network. Otherwise, thetechnique sets the initially optimized prices as the initial decisionprices for the product network. Then the technique repeatedly updates aprice change fraction while the price change fraction is greater than apredefined minimum value. For each price change fraction, the techniquecomputes a best-so-far price set. Once the price-change fraction hasreached the minimum, the best-so-far price set becomes the best solutionset.

To compute the best-so-far price set, the technique first computes aninitial monetary value and a value-plus-constraints value for theinitial decision set. The value-plus-constraints value is designed toassign a monetary value to the combination of revenue, profit, and rulecompliance by answering the question: How many dollars of revenue-profitvalue is a violation of a flexible business rule worth? In particular,the value-plus-constraints value is computed for a set of decisionprices by starting with the optimal revenue-profit monetary value forthe decision set. That is, the maximum revenue-profit value withoutregard for any business rules. Then for each decision price thatviolates a business rule constraint, a penalty value is added to thevalue. After computing the initial monetary value andvalue-plus-constraints value, the technique repeatedly selects adecision price from the decision prices. From the selected decisionprice, the technique computes both a positive changed price and anegative changed price to compute an updated monetary value and anupdated value-plus-constrains value. From this the technique candetermine whether to include the positive changed price or the negativechanged price in the decision price set.

The penalty value, in general can be computed based on a penaltyfunction, such as p(ε/P^(opt)), where a constraint violation ε is scaleddown by the optimal price P^(opt). The result of the penalty function isthen scaled by the optimal revenue-profit value yielding a constraintpenalty value of V^(opt)×p(ε/P^(opt)).

Once the price optimized solution sets are identified, the technique canoutput information about the price optimized solution sets. In somecases, the output can be a point on a revenue-profit frontier curve.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIGS. 1A and 1B illustrate exemplary system embodiments;

FIG. 2 illustrates an exemplary method embodiment for retail priceoptimization;

FIG. 3 illustrates an exemplary method embodiment for generating productnetworks;

FIG. 4 illustrates an exemplary method embodiment for generatingrecommended price sets for a collection of weights, such asrevenue-profit weights;

FIG. 5 illustrates an exemplary revenue-profit frontier curve;

FIG. 6 illustrates an exemplary method embodiment for generating one ormore price-optimized solution sets for a product network correspondingto a selected revenue-profit weight;

FIG. 7 illustrates an exemplary method embodiment for generating eithera constrained or unconstrained best solution set; and

FIG. 8 illustrates an exemplary method embodiment for generatingbest-so-far prices.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

The present disclosure addresses the need in the art for an improvedtechnique of generating recommended prices for retail products. Usingthe present technology it is possible to generate recommended prices fora group of products that optimize revenue and profit while respectingbusiness rules. The disclosure first sets forth a discussion of a basicgeneral purpose system or computing device in FIGS. 1A and 1B that canbe employed to practice the concepts disclosed herein before turning toa discussion of relevant terms and a more detailed description of theimproved technique for generating recommended prices.

FIG. 1A and FIG. 1B illustrate exemplary possible system embodiments.The more appropriate embodiment will be apparent to those of ordinaryskill in the art when practicing the present technology. Persons ofordinary skill in the art will also readily appreciate that other systemembodiments are possible.

FIG. 1A illustrates a conventional system bus computing systemarchitecture 100 wherein the components of the system are in electricalcommunication with each other using a bus 105. Exemplary system 100includes a processing unit (CPU or processor) 110 and a system bus 105that couples various system components including the system memory 115,such as read only memory (ROM) 120 and random access memory (RAM) 125,to the processor 110. The system 100 can include a cache of high-speedmemory connected directly with, in close proximity to, or integrated aspart of the processor 110. The system 100 can copy data from the memory115 and/or the storage device 130 to the cache 112 for quick access bythe processor 110. In this way, the cache can provide a performanceboost that avoids processor 110 delays while waiting for data. These andother modules can control or be configured to control the processor 110to perform various actions. Other system memory 115 may be available foruse as well. The memory 115 can include multiple different types ofmemory with different performance characteristics. The processor 110 caninclude any general purpose processor and a hardware module or softwaremodule, such as module 1 132, module 2 134, and module 3 136 stored instorage device 130, configured to control the processor 110 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 110 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction with the computing device 100, an inputdevice 145 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 135 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 100. The communications interface140 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 130 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 125, read only memory (ROM) 120, andhybrids thereof.

The storage device 130 can include software modules 132, 134, 136 forcontrolling the processor 110. Other hardware or software modules arecontemplated. The storage device 130 can be connected to the system bus105. In one aspect, a hardware module that performs a particularfunction can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 110, bus 105, display 135, and soforth, to carry out the function.

FIG. 1B illustrates a computer system 150 having a chipset architecturethat can be used in executing the described method and generating anddisplaying a graphical user interface (GUI). Computer system 150 is anexample of computer hardware, software, and firmware that can be used toimplement the disclosed technology. System 150 can include a processor155, representative of any number of physically and/or logicallydistinct resources capable of executing software, firmware, and hardwareconfigured to perform identified computations. Processor 155 cancommunicate with a chipset 160 that can control input to and output fromprocessor 155. In this example, chipset 160 outputs information tooutput 165, such as a display, and can read and write information tostorage device 170, which can include magnetic media, and solid statemedia, for example. Chipset 160 can also read data from and write datato RAM 175. A bridge 180 for interfacing with a variety of userinterface components 185 can be provided for interfacing with chipset160. Such user interface components 185 can include a keyboard, amicrophone, touch detection and processing circuitry, a pointing device,such as a mouse, and so on. In general, inputs to system 150 can comefrom any of a variety of sources, machine generated and/or humangenerated.

Chipset 160 can also interface with one or more communication interfaces190 that can have different physical interfaces. Such communicationinterfaces can include interfaces for wired and wireless local areanetworks, for broadband wireless networks, as well as personal areanetworks. Some applications of the methods for generating, displaying,and using the GUI disclosed herein can include receiving ordereddatasets over the physical interface or be generated by the machineitself by processor 155 analyzing data stored in storage 170 or 175.Further, the machine can receive inputs from a user via user interfacecomponents 185 and execute appropriate functions, such as browsingfunctions by interpreting these inputs using processor 155.

It can be appreciated that exemplary systems 100 and 150 can have morethan one processor 110 or be part of a group or cluster of computingdevices networked together to provide greater processing capability.

Having disclosed some components of a computing system, the disclosurenow turns to a brief description of terms relevant to retail science.

Product Grouping: distinct products are items on retail shelves oronline perceived as different by shoppers. The products can be groupedinto different types of groupings. The process of identifying pricefamilies and product networks is called product linking.

Price Families: products can be grouped into price families. Each pricefamily can have products that share similar shopper response to pricechange and require identical prices. For example, six-ounce (6 oz.)containers of Dannon™ Yogurt in various flavors, such as, plain,vanilla, strawberry and strawberry-banana, would be in one price familysince they share similar shopper demand and are priced the same.

Product Networks: price families can be grouped into product networks.Each product network can have price families that are linked together byat least one comparative business rule. For, example, Coke™ products ofdifferent sizes, Pepsi™ products of different sizes, and store-brandsoda products of different sizes can be linked together by one or morecomparative rules. For example, a twelve-ounce (12 oz.) can of Coke™ anda twelve-ounce (12 oz.) can of Pepsi™ may be the same price and each maybe twenty percent (20%) more expensive than a twelve-ounce (12 oz.) canof the store brand cola soda. Similarly, a sixteen-ounce (16 oz.)container of Coke™ may be ten percent (10%) more expensive than atwelve-ounce (12 oz.) can of Coke™. In another example, a sixteen-ounce(16 oz.) container of Pepsi™ may be thirty percent (30%) more expensivethan a twelve ounce (12 oz.) of the store brand cola soda.

Store Grouping: stores are different places to shop. An online retailenvironment can be considered to be one store. The process ofidentifying markets, zones, locations, and areas is called storelinking. Markets can be a collection of stores with common shoppers,typically defined geographically, but can be defined demographically.Zones can be collections of markets defined by retailers for one or morereasons. Locations can be collections of one or more stores defined bythe granularity of available sales data. Areas can be collections of oneor more locations defined by the granularity of price decisions. Whileretail-science can have areas as disjoint collections of locations, anarea for a product can be different for two different promotions. Forexample, a first soda retailer can have city-by-city pricing for theirsodas and a second soda retailer can have statewide pricing.

Customer Types: communities of shoppers identified in a pricingenvironment. For example, a product might be offered at a discount for agroup of people, such as club members, people over a specific age andstudents.

Sales Types: can be different types of selling promotions which caninclude no promotions, signage, products at the end of an aisle(“endcaps”), discount and buy some, get some free.

Decision Prices: individual price decisions can be based on pricefamily, area, customer type and sales type.

Decision Price Sets: decision prices can be grouped into decision pricesets based on one or more comparative business rules, such as size orbrand.

Bounded Business Rules: can be ranges imposed on individual decisionprices. Examples can include competitive prices (price of a productbased on competitor's price), margin requirements (no less than costplus a set percentage of the price), price-change amounts (no changemore than a set percentage or amount or no change less than a setpercentage or amount) and locked decision price (price cannot changefrom its current price). Retailers can also have bounded business ruleswith lower bounds based on competition and upper bounds based on margin.

Comparative Business Rules: can be relationships between two decisionprices. For example, a size rule where the price for one product havinga first size can have a fixed rate higher than the same product having asecond size, such as a twenty ounce (20 oz.) bag of chips can cost 1.6times higher than a ten ounce (10 oz.) bag of chips). In anotherexample, a brand rule where one brand of a product can have a fixed ratehigher than a second brand of the same product, such as the price for anational brand soda can be 1.3 times higher than a store brand soda can.Decision prices can be bounded by two comparative business rules, suchas brand and size.

Maximum Price Change: price changes for a product can only be changed bya certain percentage or amount from the current price, such as themanufactured recommended price.

Minimum Price Change: price changes for a product can only be changed bya certain percentage or amount from the current price, such as themanufactured recommended price.

Margin: the price for a product required to earn a minimum margin.Margin can be profit divided by revenue or price minus cost divided byprice.

Locked Prices: a price that cannot be changed.

Competitive Pricing: a price of a same or similar product offered byanother retailer can be a factor in setting a price for the product. Forexample, a retailer may require a price of a product be lower than asame product sold by a competing retailer. In another example, aretailer may require a price of a product be within a percentage of theprice offered by a competing retailer, e.g. five percent lower so thatthe price is lower, but not too low.

Size: two products that are the same, but different sizes, can have aprice relationship. Typically, the larger sized product is moreexpensive than the smaller sized product, but is less expensive on aunit basis. For example, a twenty ounce (20 oz.) container for a firstproduct would have a fixed price relationship with a ten ounce (10 oz.)container for the first product, such as 1.5 or 1.8 times moreexpensive.

Brand: two products that are similar but different brands can have aprice relationship. Typically, one brand is considered superior to theother and there is a range of price ratios based on shopper perception.For example, a brand name cereal would be between 1.3 and 1.6 times asexpensive as the store-brand version. When different brand products aredifferent sizes, the constraint can be considered a brand constraint andthe ratios combine in the least-restrictive way. For example, a twentyounce (20 oz.) box a name brand cereal should be between (1.3×1.5) and(1.8×1.6) as expensive as a ten ounce (10 oz.) box of the store-brandversion.

Product Line: two products that are the same brand, but different inanother way, can have a price relationship. Typically, the same companyor brand can offer different products under the same name/brand with afixed price relationship. For example, a dairy company can offer atwenty ounce (20 oz.) container of ice cream to have a fixed pricerelationship with a twenty ounce (20 oz.) container of frozen yogurt,such as 1.2 or 1.3 times more expensive.

Cannibalization (Substitution): occurs when the sales of a first productdetract from the sales of a second product. For example, when Coke™ goeson sale, its increased sales decrease the sales of Pepsi™.

Affinity (Halo Affect): occurs when the sales of one product enhance thesales of another product. For example, when the sales of hot dogs leadto increased sales of buns, relish, charcoal, etc.

Promotion: products on sale, or offered to specific customer groups,must be offered at a lower price compared to the recommended price. Forexample, the lower price can be for ten percent (10%) off or buy one getone free (BOGO).

Ending Numbers: product prices can be required to end in appropriatevalues. Typically, prices end with a nine (9) as the last digit in theprice, such as “79” or “99.” In one or more cases, the last two digitsof the price can indicate the status of the product. For example, aproduct that ends in “99” can be for products that are always sold bythe store, a product that ends in “89” can be for products that areseasonal, and a product that ends in “79” can be for products that willno longer be carried once they are sold out.

Multiples: products that come in multiples can have different pricingrules than single-item prices that end with a “9” for the last digit forthe price. For example, when multiples of a product are offered, theprice can end with two zeros (“00”), such as three (3) for three dollars($3.00) or five (5) for five dollars ($5.00).

Number of Price Changes: a global rule for an assignment of decisionprices may involve a limit on the number or percentage of prices thatcan change. For example, no more than twenty percent (20%) of the pricescan change from current to recommended prices.

Global Margin: a global rule for the entire collection of decisionprices can be expected global margin, which is that total profit (priceminus cost) divided by total price should be above a specified value.

Revenue-Profit Weight: defines a business preference for profit overrevenue. A value of zero for the revenue-profit weight means a retaileris aiming for maximum revenue in price assignment. A value of one forthe revenue-profit weight means a retailer is aiming for maximum profitin price assignment. A value w between the zero and one means a retaileris aiming to maximize (1−w)R+wP, where R is revenue and P is profit.

Sales History Data: each entry in the sales history can specify aproduct or price family, a location, a customer type, a sales type, atime period, and/or numerical data such as price, costs, and/or unitssold. Time periods can typically be in weeks with date ranges. Due tothe nature of point of sales (POS) systems, the sales history typicallydoes not contain zero sales information. It only contains data when apositive quantity is sold. Sales history can be for one or moregroupings of products, all products offered for sale, or a subset of theproducts offered for sale. Sales history can cover one or morelocations, all locations, or a subset of locations selling the products.Time periods can be coincident, overlapping, or non-overlapping fordifferent groupings of products and locations.

Having defined a glossary of relevant terms, the disclosure now turns toa brief introductory description of using retail price optimization togenerate recommended prices for retail products. In retail priceoptimization, a system can use demand modeling to analyze sales historydata to generate a demand model, or demand-model parameters, of consumerdemand for products. The system can then use the model to compute pricesthat maximize revenue, profit, or a combination of revenue and profit.

FIG. 2 is a flowchart illustrating an exemplary retail priceoptimization method 200. For the sake of clarity, this method isdiscussed in terms of an exemplary computing system, such as is shown inFIGS. 1A and 1B. Although specific steps are shown in FIG. 2, in otherembodiments a method can have more or less steps than shown.

The retail price optimization begins when the computing system appliesdemand modeling to sales history data to produce model parameters (202),which can be used in a variety of retail-science analyses, includingforecasting sales, price optimization, markdown, assortment planning,inventory replenishment, promotion planning, etc. In some embodiments,the predictions can be by product, store, time, and promotion status.The sales data can specify a variety of information about one or moreproducts over a sequence of time periods. For example, the sales datacan specify the number of sales per week over a two year time period.

The demand model can be a publically known demand model or a proprietarydemand model. The model parameters can include economic demandelasticity and baseline sales. The model parameters can also includefactors for seasonality and/or for specific local, such as regional andnational events and holidays. In one or more embodiments, one or moredemand models can have specific multipliers to account for promotionalor sales events. The sales data can be for a common duration, such astwo months or two years.

After applying demand modeling to sales data to produce demand-modelparameters (or a demand model), the computing system can use thedemand-model parameters in price optimization to produce recommendedprices (204). The recommended prices can be for the decision pricescorresponding to the sales history data. In some embodiments, the priceoptimization can be applied based on the model parameters and otherinputs. The other inputs can include one or more of current prices,bounded business rules, and/or comparative business rules. Therecommended prices can be a single list of prices or list of prices forone or more revenue-profit weights. As a result, the recommended pricescan indicate revenue-profit goals, ending-number price compliance,common prices for price families, compliance with bounded businessrules, compliance with comparative business rules, global marginrequirements, and/or price-change requirements. In one or moreembodiments, the computing system can forecast unit sales from currentprices and recommended prices using the demand-model parameters. Thecomputing system can also use the demand-model parameters for markdownanalysis, assortment planning, inventory replenishment, promotionplanning, etc.

After using the model parameters and other inputs in price optimizationto produce recommended prices, the computing system can output therecommended prices (206). In some embodiments, the computing system canoutput the forecast unit sales from current prices and recommendedprices. The output can be in one or more forms, including, but notlimited to, rendering the recommended prices on a display, printed, andstored in one or more databases communicatively coupled to theprocessor. The recommended prices can be one set of decision-pricerecommendations or represent several revenue-profit weights.

As described above, price optimization is a key step in the process ofgenerating recommended prices. Using a demand model, prices can beoptimized within each product network to maximize revenue, profit, or acombination of revenue and profit. The result is a set of recommendedprices in which one or more products can be assigned a price differentthan an original price of the product. In theory, such an approachproduces an ideal set of prices that maximize revenue and/or profit.However, the set of recommended prices may be unworkable in a realisticbusiness setting.

One problem that storeowners can face is that the generated recommendedprices can violate one or more business rules. The business rules cangovern a single price, such as minimum or maximum price change, margin,locked prices, competitive prices, ending numbers, and multiples. Thebusiness rules can also define relationships between different productsthus involving two or more decision prices, such as size, brand, productline, and promotion. Determining recommended prices becomes moredifficult with each business rule that needs to be followed. Forexample, price relationships between different sizes of the same productand different, interchangeable brands can make it difficult to optimizerevenue and profit. The presently disclosed price optimization techniqueaddresses this limitation by assigning a reasonable monetary value toeach rule and resolving the combination of revenue, profit, and rulecompliance. That is, the price optimization technique can generaterecommended price sets that optimize revenue and profit while complyingwith a set of business rules by assigning a monetary value to eachbusiness rule.

The price optimization technique can be applied to networks ofrule-related product prices. In general, the present price optimizationtechnique generates a set of recommended prices for a collection ofproduct networks by first obtaining constraint determined networks, orproduct networks. The technique then generates a collection ofprice-optimized solution sets for a product network by optimizing adecision price set for the product network based on one or morerevenue-profit weights. The technique optimizes the decision price setby repeatedly selecting a revenue-profit weight. For each selectedrevenue-profit weight, the technique generates a constrained bestsolution set and an unconstrained best solution set. The techniquegenerates the constrained best solution set based on a first set ofbusiness rules that include at least one flexible or optional businessrules, e.g., maximum price change, margin, competitive pricing, size,brand, product line, and promotion; and the unconstrained best solutionset based on a second set of business rules. Both the first and secondset of business rules can include non-negotiable or hard business rules,e.g., minimum price change, locked process, ending numbers, andmultiples. The constrained best solution is the recommend price set fora selected weight and the unconstrained best solution is the optimalprice set for the selected weight.

The price optimization technique operates on a product network. FIG. 3is a flowchart illustrating an exemplary method 300 for generatingproduct networks. For the sake of clarity, this method is discussed interms of an exemplary computing system, such as is shown in FIGS. 1A and1B. Although specific steps are shown in FIG. 3, in other embodiments amethod can have more or less steps.

Method 300 begins when the computing system obtains a collection ofproducts and stores (302). Price family groupings of products and zoneand vendor groupings of stores define decision prices. To generatedecision prices the computing system can group the products into one ormore price families and the stores into one or more store groupings(304). After identifying product families and store groupings, thecomputing system can applying one or more business rules that define arelationship between decision prices to the groupings to generateproduct networks (306). For example, products with prices related bycomparative business rules, such as size, quantity, brand, product line,or promotion, can be grouped into the same product network. In somecases, the computing system can represent the decision prices as nodesin a graph, and then the business rule constraints can be edgesconnecting the nodes. Each connected sub-graph within the graph thenrepresents a product network.

FIG. 4 is a flowchart illustrating a method 400 for generatingrecommended price sets for a product network across a set ofrevenue-profit weights. In particular, method 400 can be used togenerate recommended price sets that optimize revenue and profit whilecomplying with a set of business rules. For the sake of clarity, thismethod is discussed in terms of an exemplary computing system, such asis shown in FIGS. 1A and 1B. Although specific steps are shown in FIG.4, in other embodiments a method can have more or less steps.

Method 400 begins when the computing system obtains a product network(402). The computing system can obtain the product network by generatingthe product network using a variety of techniques, including method 300in FIG. 3. In some embodiments, one or more product networks can bepre-computed and stored. Alternatively, one or more product networks canbe computed as part of method 400. When there is more than one productnetwork, method 400 can be applied iteratively to each of the one ormore product networks.

The computing system can also define a set of revenue-profit weights(404. For example, a revenue-profit weight scheme can be defined with arange of zero to one using 0.02 increments. In this case, the schemewould define a set of 51 revenue-profit weights. The revenue-profitweight scheme can also be dynamic, where a weight selection depends onthe history of prior optimizations. For example, if weights 0.2 and 0.4yield essentially the same revenue and profit and a weight of 0.6 issignificantly different, then a subsequently selected weight can be 0.5rather than 0.3.

The computing system can generate one or more price-optimized solutionsets for each product network by first selecting a weight from the setof weights (406). Then the computing system can optimize the decisionset for the product network based on the selected weight to generate aprice-optimized solution set corresponding to the weight (408). Thecomputing system can define one or more price-optimizations, such as arevenue-profit optimization including method 600 in FIG. 6, describedbelow. The computing system can apply the one or moreprice-optimizations to generate a plurality of price-optimized solutionsets for each product network by optimizing the decision price set foreach product network. In some cases, the one or more price-optimizationscan be applied independently such that a first price-optimizationgenerates a first plurality of price-optimized solution sets and asecond price-optimization generates a second plurality ofprice-optimized solution sets. The one- or more price-optimizations canalso be applied in conjunction with each other. For example, a firstprice-optimization can generate a plurality of price-optimized solutionsets. Then a second price-optimization can be applied to each of thoseprice-optimized solution sets. Additionally, in some cases, aprice-optimization can define a set of weights or optimization factors.These weights or factors can be used to generate multipleprice-optimization solution sets from a single decision price set. Onepossible price-optimization is a revenue-profit optimization. Therevenue-profit optimization can define a set of revenue-profit weights.

After generating the price-optimized solution set for a (productnetwork, weight) pair, the computing system can check if there are moreweights (410). In some cases, checking for more weights can includecomputing a new weight, such as when the weights are dynamic. If so, thecomputing system repeats the steps of selecting a weight (406), andgenerating a price-optimized solution set corresponding to the selectedweight for the product network (408).

If all of the weights have been processed, the computing system canoptionally apply a maximum number price changes optimization (412) priorto outputting the price-optimized solution sets (414). The output canoccur in one or more forms, including saving, rendering, and/or printingthe information. The computing system can save a file comprising theinformation. The file can be saved in memory associated with thecomputing system, such as in a database communicatively coupled to aprocessor in the computing system. The computing system can render theinformation on a display communicatively to the computing system. Thecomputer system can transmit the information to a printercommunicatively coupled to the computing system. In some embodiments,the output can be a frontier curve in revenue and profit. For example,FIG. 5 illustrates a revenue-profit frontier curve for a sequence ofrevenue-profit weights. A retailer can use a frontier curve to select asolution that meets a global margin level.

FIG. 6 is a flowchart illustrating an exemplary method 600 forgenerating one or more price-optimized solution sets for a productnetwork corresponding to a selected revenue-profit weight. Inparticular, method 600 can be used to generate price-optimizedrecommended prices that optimize revenue and profit while adhering toone or more business rules. For the sake of clarity, this method isdiscussed in terms of an exemplary computing system, such as is shown inFIGS. 1A and 1B. Although specific steps are shown in FIG. 6, in otherembodiments a method can have more or less steps.

The price-optimization method 600 can begin when the computing systemgenerates a constrained best solution set for the selected (productnetwork, revenue-profit weight) pair (602). Each value in a priceoptimized solution set has a monetary value. The monetary value can bedefined for a revenue-profit weight W. For example, for a revenue-profitweight, monetary value can be defined asValue=((1−W)×Revenue)+(W×Profit)). When the computing system generates aconstrained best solution set, the computing system adjusts the monetaryvalue to trade off revenue and profit against one or more business ruleconstraints, including flexible or optional business rules, e.g.,maximum price change, margin, competitive pricing, size, brand, productline, and promotion; and hard or non-negotiable business rules, e.g.,minimum price change, locked process, ending numbers, and multiples. Theconstrained best solution set can be generated using a variety oftechniques, including hill climb method 700 in FIG. 7, described below.After generating the constrained best solution set, the computing systemcan store the solution set as the recommended prices for the selectedweight (604).

The computing system can also generate an unconstrained best solutionset for the selected revenue-profit weight (606). When the computingsystem generates an unconstrained best solution set, the computingsystem adjusts the monetary value to trade off revenue and profitagainst one or more hard or non-negotiable business rules, e.g. minimumprice change, locked process, ending numbers, and multiples. As with theconstrained best solution set, the unconstrained best solution set canbe generated using a variety of techniques, including hill climb method700 in FIG. 7, described below. After generating the unconstrained bestsolution set, the computing system can store the solution set as theoptimal prices for the selected weight (608). After storing both theconstrained and unconstrained best solution sets, the computing systemcan resume previous processing, which can include resuming processing atstep 408 of method 400 in FIG. 4.

FIG. 7 is a flowchart illustrating an exemplary hill climb method 700for generating either a constrained or unconstrained best solution set.For the sake of clarity, this method is discussed in terms of anexemplary computing system, such as is shown in FIGS. 1A and 1B.Although specific steps are shown in FIG. 7, in other embodiments amethod can have more or less steps.

The hill climb method 700 can begin when the computing system obtainscurrent prices (702) and calculates a current monetary value based onthe current prices (704). The computing system also obtains initiallyoptimized prices for the selected revenue-profit weight (706) andcalculates an initially optimized monetary value based on the initiallyoptimized prices (708). The computing system calculates both a currentmonetary value and an initially optimized monetary value to identifywhich is better in step 710, below. It is possible that a price-change,margin, or other rule enforced in the optimization can lead to a lowerrevenue-profit value even in the presence of optimizations. When thisoccurs the current monetary value can be better than the optimizedmonetary value.

Once the computing system has both the current monetary value and theinitially optimized monetary value, the computing system can check ifthe initially optimized monetary value is less than the current monetaryvalue (710). If so, the computing system can set the current prices asthe initial decision prices for the product network (712). Otherwise,the computing system can set the initially optimized prices as theinitial decision prices for the product network (714). The computingsystem can also initialize the best-so-far prices using the initialdecision prices.

After setting the initial decision prices for the product network, thecomputing system can set an initial value for a price change fraction(716), which the computing system can use to adjust a decision price. Apossible initial value can be 0.1. The computing system can then checkif the price change fraction is greater than a predefined minimum value(718), such as 0.01.

If the price change fraction is still greater than a predefined minimumvalue, then the computing system can compute best-so-far prices (720).The best-so-far prices can be generated using a variety of techniques,including method 800 in FIG. 8, described below.

After computing the best-so-far prices, the computing system can updatethe price change fraction (722). A technique for updating the pricechange fraction can be to multiply the current price change fraction bya predefined value. For example, price change fraction=price changefraction×0.7.

After updating the price change fraction, the computing system can againcheck if the price change fraction is greater than a predefined minimumvalue (718). If not, the computing system can set best prices ascomputed best-so-far prices (724). The computing system can then resumeprevious processing, which can include resuming processing at steps 602or 606 of method 600 in FIG. 6.

FIG. 8 is a flowchart illustrating an exemplary method 800 forgenerating best-so-far prices. For the sake of clarity, this method isdiscussed in terms of an exemplary computing system, such as is shown inFIGS. 1A and 1B. Although specific steps are shown in FIG. 8, in otherembodiments a method can have more or less steps.

The best-so-far prices method can begin when the computing systemobtains initial decision prices for the product network (802). Using theinitial decision prices, the computing system can compute a monetaryvalue for the (product network, selected weight) pair (804) and assignthe computed monetary value as an initial monetary value (806). Themonetary value can be a revenue-profit value, such asValue=((1−W)×Revenue)+(W×Profit)).

Using the initial decision prices again, the computing system cancompute a value-plus-constraints value for the (product network,selected weight) pair (808) and assign the computedvalue-plus-constraints value as a best value-plus-constraints value(810). The value-plus-constraints value is designed to assign a monetaryvalue to the combination of revenue, profit, and rule compliance byanswering the question: How many dollars of revenue-profit value is aviolation of a flexible business rule worth? In particular, thevalue-plus-constraints value is computed for a set of decision prices bystarting with the optimal revenue-profit monetary value for the decisionset. That is, the maximum revenue-profit value without regard for anybusiness rules. Then for each decision price that violates a businessrule constraint, a penalty value is added to the value.

In general, a penalty value can be computed based on a penalty function,such as p(ε/P^(opt)), where a constraint violation e is scaled down bythe optimal price P^(opt). The result of the penalty function is thenscaled by the optimal revenue-profit value yielding a constraint penaltyvalue of V^(opt)×p(ε/P^(opt)). This scaling heuristic uses the optimalprice P^(opt) and revenue-profit value V^(opt) to access a monetaryvalue of a constraint violation scaled to revenue and profit. It allowsthe penalty function p(x) to be independent of price and value across acollection of varying products and stores. If the violated constrainthas only one decision price, e.g., minimum price change, maximum pricechange, margin, locked prices, ending numbers, etc., the violationamount is directly applied. However, if the violated constraint involvestwo decision prices, e.g., size, branch, product line, promotion, etc.,then half of the violation amount is applied to each of the two decisionprices to achieve a “meet in the middle” approach. For example, supposeproduct A is $1.20 and product B is $2.00 and that the business rulestates that product B should be twice the price of product A. Thenproduct A has a constraint violation of $0.20 and product B has aconstraint violation of $0.40. By applying half the violation amount toeach we end up applying $0.10 to product A and $0.20 to product B.

After computing the initial monetary value and value-plus-constraintsvalue, the computing system can select a decision price from thedecision prices (812). From the selected decision price, the computingsystem can compute both a positive changed price (814) and a negativechanged price (826). Staring with the positive changed price, thecomputing system can positively adjust the selected decision price bythe current price change fraction. For example, the computing system canadd the current price change fraction to the decision price, i.e.positive changed price=decision price+price change fraction. Thecomputing system can then adjust the positive changed price to enforceany non-negotiable rules (816), such as minimum price change, lockedprices, ending numbers, and multiples. Using the decision pricesincluding the positive changed price, the computing system can computean updated monetary value (818) and an updated value-plus-constraintsvalue (820).

After computing the updated monetary value and updatedvalue-plus-constraints value, the computing system can compare them withthe initial monetary value and the best value-plus-constraints value.The computing system checks if the updated value-plus-constraints valueis greater than the best value-plus-constraints value and the updatedmonetary value is greater than or equal to the initial monetary value(822). If so, the computing system assigns the decision set includingthe positive changed price to the best-so-far prices (824).

For the negative changed price, the computing system can negativelyadjust the selected decision price by the current price change fraction.For example, the computing system can subtract the current price changefraction from the decision price, i.e. negative changed price=decisionprice−price change fraction. The computing system can then adjust thenegative changed price to enforce any non-negotiable rules (828), suchas minimum price change, locked prices, ending numbers, and multiples.Using the decision prices including the negative changed price, thecomputing system can compute an updated monetary value (830) and anupdated value-plus-constraints value (832).

After computing the updated monetary value and updatedvalue-plus-constraints value, the computing system can compare them withthe initial monetary value and the best value-plus-constraints value.The computing system checks if the updated value-plus-constraints valueis greater than the best value-plus-constraints value and the updatedmonetary value is greater than or equal to the initial monetary value(834). If so, the computing system assigns the decision set includingthe positive changed price to the best-so-far prices (836).

After evaluating both positive and negative changed price options, thecomputing system checks if there is another decision price in thedecision prices to evaluate (838). If so, the computing system selects anext decision price (812). Once all decision prices have been evaluated,the computing system has determined the best-so-far prices for theselected weight. The computing system can then resume previousprocessing, which can include resuming processing at step 720 of method700 in FIG. 7.

It should be noted that in the case where the computing system isperforming best-so-far prices method 800 with no flexible or optionalbusiness rules, the monetary value and the value-plus-constraints valuefor the (product network, selected weight) pair would be the same. Thiscan occur when the computing system arrives at method 800 as a result ofcalculating unconstrained prices at step 606 of method 600 in FIG. 6.

The present technology can take the form of hardware, software or bothhardware and software elements. In some implementations, the technologycan be implemented in software, which includes but is not limited tofirmware, resident software, microcode, a Field Programmable Gate Array(FPGA), graphics processing unit (GPU), or Application-SpecificIntegrated Circuit (ASIC). In particular, for real-time or nearreal-time use, an FPGA or GPU implementation would be desirable.

Furthermore, portions of the present technology can take the form of acomputer program product comprising program modules accessible fromcomputer-usable or computer-readable medium storing program code for useby or in connection with one or more computers, processors, orinstruction execution system. For the purposes of this description, acomputer-usable or computer readable medium can be any apparatus thatcan contain, store, communicate, propagate, or transport the program foruse by or in connection with the instruction execution system,apparatus, or device. The medium can be non-transitory (e.g., anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system (or apparatus or device)) or transitory (e.g., apropagation medium). Examples of a non-transitory computer-readablemedium include a semiconductor or solid state memory, magnetic tape, aremovable computer diskette, a random access memory (RAM), a read-onlymemory (ROM), a rigid magnetic disk and an optical disk. Currentexamples of optical disks include compact disk—read only memory(CD-ROM), compact disk—read/write (CD-R/W) and DVD. Both processors andprogram code for implementing each as aspect of the technology can becentralized or distributed (or a combination thereof) as known to thoseskilled in the art.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Examples within the scope of the present disclosure may alsoinclude tangible and/or non-transitory computer-readable storage mediafor carrying or having computer-executable instructions or datastructures stored thereon. Such non-transitory computer-readable storagemedia can be any available media that can be accessed by a generalpurpose or special purpose computer, including the functional design ofany special purpose processor as discussed above. By way of example, andnot limitation, such non-transitory computer-readable media can includeRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to carry or store desired program code means in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information is transferred or provided over a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection canbe properly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other examples of thedisclosure may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Examples may also be practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination thereof) through a communications network. Ina distributed computing environment, program modules may be located inboth local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. For example, the principles herein apply not only to asmartphone device but to other devices capable of receivingcommunications such as a laptop computer. Those skilled in the art willreadily recognize various modifications and changes that may be made tothe principles described herein without following the exampleembodiments and applications illustrated and described herein, andwithout departing from the scope of the disclosure.

What is claimed is:
 1. A computer-implemented method comprising: obtaining a product network having a decision price set; and generating, via processor, a plurality of price-optimized solution sets for the product network by optimizing the decision price set based on a plurality of revenue-profit weights as follows: repeatedly selecting a revenue-profit weight for a number of revenue-profit weights, and for each weight: generating a constrained best solution set by adjusting a monetary value based on a first set of business rules, wherein the first set of business rules include at least one flexible business rule, and storing the constrained best solution set as a recommended price set for the selected weight, and generating an unconstrained best solution set by adjusting a monetary value based on a second set of business rules, and storing the constrained best solution set as an optimal price set for the selected weight.
 2. The computer-implemented method of claim 1, wherein generating a best solution set comprises: calculating a current monetary value based on an obtained current price set; calculating an initially optimized monetary value based on an obtained initially optimized price set; comparing the current monetary value to the initially optimized monetary value to select an initial decision price set, wherein the initial decision price set is the current price set when the initially optimized monetary value is less than the current monetary value and the initially optimized price set otherwise; and repeatedly updating a price change fraction while the price change fraction is greater than a predefined minimum value, and for each price change fraction: computing a best-so-far price set.
 3. The computer-implemented method of claim 2, wherein computing the best-so-far price set comprises: computing an initial monetary value for the initial decision set; computing a value-plus-constraints value for the initial decision price set based on a set of business rules; repeatedly selecting a decision price from the decision price set, and for each decision price: computing a positive changed price based on the decision price and the price change fraction, wherein the positive changed price conforms to any non-negotiable business rules, computing an updated monetary value and an updated value-plus-constraints value based on the decision price set including the positive changed price, in the event that the updated value-plus-constraints value is greater than a best value-plus-constraints value and the updated monetary value is at least as great as the initial monetary value, assigning the decision price set including the positive changed price as the best-so-far price set, computing a negative changed price based on the decision price and the price change fraction, wherein the negative changed price conforms to any non-negotiable business rules, computing an updated monetary value and an updated value-plus-constraints value based on the decision price set including the negative changed price, and in the event that the updated value-plus-constraints value is greater than a best value-plus-constraints value and the updated monetary value is at least as great as the initial monetary value, assigning the decision price set including the negative changed price as the best-so-far price set.
 4. The computer-implemented method of claim 3, wherein computing a value-plus-constraints value for a decision price set comprises: computing a monetary value for the decision set; and adding a constraint penalty value to the monetary value when a decision price in the decision price set violates a business rule.
 5. The computer-implemented method of claim 4, wherein the constraint penalty value is computed based on a penalty function p(ε/P^(opt)) scaled by V^(opt), wherein ε is a constraint violation, P^(opt) is an optimal price, and V^(opt) is an optimal revenue-profit value.
 6. The computer-implemented method of claim 4, wherein for a violated business rule with multiple decision prices, the constraint penalty value is split equally and applied to each of the multiple decision prices.
 7. The computer-implemented method of claim 1, wherein a flexible business rule is at least one of maximum price change, margin, competitive pricing, size, brand, product line, or promotion, and a non-negotiable business rule is at least one of minimum price change, locked prices, ending numbers, or multiples.
 8. A manufacture comprising: a non-transitory computer-readable storage medium; and a computer-executable instruction stored on the non-transitory computer-readable storage medium which, when executed by a computing device, causes the computing device to perform a method comprising: generating a plurality of price-optimized solution sets for a product network having a decision price set by optimizing the decision price set based on a plurality of revenue-profit weights as follows: repeatedly selecting a revenue-profit weight for a number of revenue-profit weights, and for each weight: generating a constrained best solution set by adjusting a revenue-profit value based on a first set of business rules, wherein the first set of business rules include at least one flexible business rule, and storing the constrained best solution set as a recommended price set for the selected weight, and generating an unconstrained best solution set by adjusting a revenue-profit value based on a second set of business rules, and storing the constrained best solution set as an optimal price set for the selected weight.
 9. The manufacture of claim 8, wherein generating a best solution set comprises: calculating a current revenue-profit value based on an obtained current price set; calculating an initially optimized revenue-profit value based on an obtained initially optimized price set; comparing the current revenue-profit value to the initially optimized revenue-profit value to select an initial decision price set, wherein the initial decision price set is the current price set when the initially optimized revenue-profit value is less than the current revenue-profit value and the initially optimized price set otherwise; and repeatedly updating a price change fraction while the price change fraction is greater than a predefined minimum value, and for each price change fraction: computing a best-so-far price set.
 10. The manufacture of claim 9, wherein computing the best-so-far price set comprises: computing an initial revenue-profit value for the initial decision set; computing a value-plus-constraints value for the initial decision price set based on a set of business rules; repeatedly selecting a decision price from the decision price set, and for each decision price: computing a positive changed price based on the decision price and the price change fraction, wherein the positive changed price conforms to any non-negotiable business rules, computing an updated revenue-profit value and an updated value-plus-constraints value based on the decision price set including the positive changed price, in the event that the updated value-plus-constraints value is greater than a best value-plus-constraints value and the updated revenue-profit value is at least as great as the initial revenue-profit value, assigning the decision price set including the positive changed price as the best-so-far price set, computing a negative changed price based on the decision price and the price change fraction, wherein the negative changed price conforms to any non-negotiable business rules, computing an updated revenue-profit value and an updated value-plus-constraints value based on the decision price set including the negative changed price, and in the event that the updated value-plus-constraints value is greater than a best value-plus-constraints value and the updated revenue-profit value is at least as great as the initial revenue-profit value, assigning the decision price set including the negative changed price as the best-so-far price set.
 11. The manufacture of claim 10, wherein computing a value-plus-constraints value for a decision price set comprises: computing a revenue-profit value for the decision set; and adding a constraint penalty value to the revenue-profit value when a decision price in the decision price set violates a business rule, wherein the constraint penalty value is V^(opt)×p(ε/P^(opt)), wherein e is a constraint violation, P^(opt) is an optimal price, and V^(opt) is an optimal revenue-profit value.
 12. A system comprising: a processor; and a computer-readable storage medium storing instructions for controlling the processor to perform steps comprising: obtaining a product network having a decision price set; generating a plurality of price-optimized solution sets for a product network having a decision price set by optimizing the decision price set based on a plurality of revenue-profit weights as follows: repeatedly selecting a revenue-profit weight for a number of revenue-profit weights, and for each weight: generating a constrained best solution set by adjusting a revenue-profit value based on a first set of business rules, wherein the first set of business rules include at least one flexible business rule, and storing the constrained best solution set as a recommended price set for the selected weight, and generating an unconstrained best solution set by adjusting a revenue-profit value based on a second set of business rules, and storing the constrained best solution set as an optimal price set for the selected weight; and outputting information associated with the plurality of price-optimized solution sets.
 13. The system of claim 12, wherein generating a best solution set comprises: calculating a current revenue-profit value based on an obtained current price set; calculating an initially optimized revenue-profit value based on an obtained initially optimized price set; comparing the current revenue-profit value to the initially optimized revenue-profit value to select an initial decision price set, wherein the initial decision price set is the current price set when the initially optimized revenue-profit value is less than the current revenue-profit value and the initially optimized price set otherwise; and repeatedly updating a price change fraction while the price change fraction is greater than a predefined minimum value, and for each price change fraction: computing a best-so-far price set.
 14. The system of claim 13, wherein computing the best-so-far price set comprises: computing an initial revenue-profit value for the initial decision set; computing a value-plus-constraints value for the initial decision price set based on a set of business rules; repeatedly selecting a decision price from the decision price set, and for each decision price: computing a positive changed price based on the decision price and the price change fraction, wherein the positive changed price conforms to any non-negotiable business rules, computing an updated revenue-profit value and an updated value-plus-constraints value based on the decision price set including the positive changed price, in the event that the updated value-plus-constraints value is greater than a best value-plus-constraints value and the updated revenue-profit value is at least as great as the initial revenue-profit value, assigning the decision price set including the positive changed price as the best-so-far price set, computing a negative changed price based on the decision price and the price change fraction, wherein the negative changed price conforms to any non-negotiable business rules, computing an updated revenue-profit value and an updated value-plus-constraints value based on the decision price set including the negative changed price, and in the event that the updated value-plus-constraints value is greater than a best value-plus-constraints value and the updated revenue-profit value is at least as great as the initial revenue-profit value, assigning the decision price set including the negative changed price as the best-so-far price set.
 15. The system of claim 14, wherein computing a value-plus-constraints value for a decision price set comprises: computing a revenue-profit value for the decision set; and adding a constraint penalty value to the revenue-profit value when a decision price in the decision price set violates a business rule, wherein the constraint penalty value is V^(opt)×p(ε/P^(opt)), wherein ε is a constraint violation, P^(opt) is an optimal price, and V^(opt) is an optimal revenue-profit value.
 16. The system of claim 12, wherein obtaining a product network having a decision price set comprises: obtaining a collection of products and stores; generating one or more decision prices by grouping the products into one or more price families, and grouping the stores into one or more store groupings; applying one or more business rules to the one or more decision prices to generate one or more product networks; and selecting a product network form the one or more product networks.
 17. The system of claim 16, wherein the one or more business rules are one or more comparative business rules selected from a group consisting of: size, quantity, brand, product line, and promotion.
 18. The system of claim 16, wherein applying one or more business rules to the one or more decision prices to generate one or more product networks comprises: for each of the one or more decision prices, representing the decision price as a node in a graph; for each of the one or more business rules adding an edge to the graph; and identifying one or more connected sub-graphs in the graph, a connected sub-graph defining a product network.
 19. The system of claim 12, wherein the number of revenue-profit weights is determined dynamically based on at least one previously generated price-optimized solution set.
 20. The system of claim 12, the steps further comprising: prior to outputting information associated with the plurality of price-optimized solution sets, applying a maximum number of price changes optimization to the plurality of price-optimized solution sets. 